International Journal of Innovative
Computing, Information and Control
Volume 18, Number 3, June 2022c
ICIC International ⃝2022
ISSN 1349-4198
pp. 837–849

DISASTER EVACUATION SIMULATION CONSIDERING
TRANSMISSION OF FALSE AND FACT-CHECKING INFORMATIONBY SOCIAL NETWORKING SERVICE
Hirotaka Takahashi1 , Chie Uenae2 , Yasushi Sumitani2
Yohei Kakimoto3 and Yuto Omae3
1

Research Center for Space Science, Advanced Research Laboratories
Tokyo City University
8-15-1, Todoroki, Setagaya, Tokyo 158-0082, Japanhirotaka@tcu.ac.jp

2

Department of Information and Management Systems Engineering
Nagaoka University of Technology
1603-1, Kamitomioka, Nagaoka, Niigata 940-2188, Japan
uc31113620@gmail.com; sumitaniy@aol.com
3

Department of Industrial Engineering and Management
College of Industrial Technology
Nihon University
1-2-1, Izumi, Narashino, Chiba 275-8575, Japan{ kakimoto.youhei; oomae.yuuto }@nihon-u.ac.jp

Received September 2021; revised December 2021
Abstract. As social networking services (SNSs) have become widespread in recent
years, it has become possible to collect information from and broadcast it to an unspeciﬁed number of people with ease. In particular, in the case of disasters, information
regarding safety conﬁrmation, damage status, and evacuation sites (shelters) is frequently
exchanged. In this study, we design a disaster evacuation simulation that takes accountof the exchange of information on shelters, transmission of false information on shelters,
and fact-checking information by SNS. Furthermore, we investigate the eﬀect of the transmission of false information and fact-checking information on the disaster evacuation
behavior. The simulation results pertaining to the transmission of false information andfact-checking information, suggest that it is very important to thoroughly check the sourceof the information and spread accurate information, when evacuees voluntarily spread information using SNS.
Keywords: Disaster evacuation, Evacuation behavior, Multi-agent simulation, Social
networking service

1. Introduction. With the expansion of social networking services (SNSs) in recent
years, it has become possible to broadcast and collect the information sent by an unspeciﬁed number of people, with ease. In case of the Kumamoto earthquake that occurred in
April 2016, safety conﬁrmation was proactively performed using SNSs. In particular, at
Kumamoto University, safety conﬁrmation of the students was provided using platforms
such as LINE. Therefore, it was reported that the time required for the conﬁrmation of
the safety of the students was signiﬁcantly reduced [1]. However, it was also reported
that false information (rumors), such as “a lion escaped from a zoo”, was also spread bySNSs, and the staﬀ of the zoo were kept busy with inquiry responses [2]. SNS users may
broadcast misinformation during a disaster when unfounded discourse is sent out or when
DOI: 10.24507/ijicic.18.03.837

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false information is published owing to malicious intent (e.g., the above case of “a lion
escaped from a zoo”). In both cases, it is important to investigate the veracity of themessages and then disseminate the results (which is known as fact-checking).
The lack of storage capacity in evacuation sites (shelters) is a problem in disasterevacuation in urban areas. According to the results of the inquiry of the Central Disaster
Prevention Council, implemented in 2007 [3], it was assumed that in the case of Tokyo
Bay Northern Earthquake, the capacity of the primary shelters would prove insuﬃcient forthe ∼560,000 people residing within the Tokyo area. However, if a wider area evacuation
across Tokyo was implemented, the need would be satisﬁed. Therefore, it becomes very
crucial to guide evacuees according to the situation in times of a disaster. In addition to
the conventional methods for guiding evacuees, such as wireless-activated disaster warning
systems and mass media, it may be eﬀective for the evacuees, to spread and collectinformation through SNSs.
Although SNSs can aid people in exchanging information following a disaster, it canalso cause confusion.
Many simulation studies on disaster evacuation have been conducted since the 1970s andmulti-agent simulations of disaster evacuation have been used since the 2000s (e.g., Yasufuku [4]). As models considering the exchange of information, Matsushima et al. [5] andOsaragi and Tsuchiya [6] performed multi-agent simulation that considered the exchange
of information regarding evacuation routes. Furthermore, Fujioka et al. [7] constructeda multi-agent model to select the evacuation actions based on various information, including, congestion of the evacuation routes. These simulations considered informationexchange by wireless-activated disaster warning systems, guidance by people on the evacuation routes, and direct information exchange by nearby evacuees.
One study had previously considered information exchange by SNS [8]. We developed
the disaster evacuation simulator considering the exchange of shelter information bySNSs [9], following the research of Minami and Kato [8]. By using the disaster evacuation simulator, developed by us, the eﬃciency of the evacuation was evaluated, andwe showcased how the information transmission and diﬀusion through the SNS aﬀect theevacuation behavior. To investigate the impact of the spread of information through SNSson the evacuation behavior, we did not take account of the speciﬁc disaster contents, roads,
and cities.
However, Minami and Kato [8] and Uenae et al. [9] did not consider the eﬀects of thetransmission of false information and fact-checking information on the disaster evacuation
behavior. In this study, based on Uenae et al. [9], we extended the disaster evacuation
simulation, to take account of the exchange of information on shelters, the transmissionof false information on shelters, and fact-checking information by SNSs.
The remainder of this paper is organized as follows. Section 2 provides an overview ofour simulations. Section 3 presents the simulation results of three cases: case A: the transmission of only congestion information; case B: the transmission of congestion informationand false information; case C: considering fact-checking in addition to the transmission ofcongestion information and false information. Moreover, the eﬀects of the transmission offalse information and fact-checking information on the disaster evacuation behavior are
discussed. Section 4 presents a summary of the work.
2. Overview of Simulation. We developed and implemented a multi-agent simulationthat considers the transmission of false information regarding shelters and fact-checking
to investigate the eﬀects of information exchange by SNS on evacuee behavior.

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2.1. Overall simulation design. We simulated the evacuation behavior of evacuees
making a shelter decision by considering the capacity, congestion information, and information regarding the availability of food at the shelter, by SNS. Figure 1 presents an
example of the arrangement for the evacuees and shelters. The simulation space (City)
consists of 500×500, that is, 5,000 evacuees and 25 shelters with a capacity of 100 persons
each, as well as periodic boundary conditions. To produce the deviation of the population
distribution, we set more evacuees in the center than at the edge. In total, 10 patterns of
the shelter distributions (City1, City2, . . . , City10) were predetermined and we conducteda simulation for each arrangement. When a disaster occurs at time t = 0, the evacuees
decide on the destination shelter and move accordingly (as described in Section 2.4.2) at a
speed of 1 space per unit time (1 step). If the evacuees received information regarding thedegree of congestion of the shelter and the availability of food at the shelter by SNS (Section 2.3), they could change their destination shelter (Section 2.4.1). Moreover, certain
evacuees transmitted false information through SNSs (Section 2.5.1). Some evacuees alsoveriﬁed the facts of the information and transmitted this through SNSs (Section 2.5.2).

Figure 1. Example of arrangement of evacuees and shelters (evacuationsites). The squares represent the shelters and the circles represent the evacuees.
When an evacuee arrived within 1 space from the destination shelter, they decided
whether to enter the shelter (Section 2.4.3) or not. We assumed that all evacuations hadtaken place and completed the simulation when 99% of evacuees had completed theirevacuations.
As the purpose of this study was to investigate the eﬀects of the transmission of false
and fact-checking information by SNSs on the evacuation behavior, we assumed that all
evacuees could use SNSs with certain probabilities. In the remainder of the sections, we
explain additional details of the arrangement and conditions of our simulation.
2.2. Variables. We used the index i (i = 1, 2, . . . , 5,000) for an evacuee and j (j =1, 2, . . . , 25) for a shelter.
The deﬁnitions of the variables related to evacuee i and shelter j are displayed inTable 1. Oi is the set of candidates for evacuation destinations for evacuee i. In the initialstate, all shelters are elements of Oi . oi is the current destination shelter of evacuee i. If
the destination shelter of evacuee i is undecided, the value of oi is 0. Di (j) means the
distance between evacuee i and shelter j, whereas Cj (t) means the overcapacity rate of

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shelter j at time t; that is, Cj (t) = (the number of evacuees entering at shelter j until timet)/(100: the capacity of shelter j). Ii (j) is the information of shelter j obtained by evacuee
i. In this simulation, the evacuees exchanged the following ﬁve types of information tosimplify the simulation:
0 : Shelter j is crowded, 1 : Shelter j is somewhat crowded,
2 : Shelter j is NOT crowded,
Ii (j) =(1)
3 : Shelter j is serving food, 4 : Information that shelter j is serving food is incorrect.
Fi indicates the state of receipt of false information and fact-checking information forevacuee i. Fi is used to deﬁne the following three states:
0 : Evacuee i does not read the false information or fact-checkinginformation,
Fi =
(2)
1
:
Evacueei reads only the false information, 2 : Evacuee i reads the fact-checking information.
Finally, Ui is the probability that evacuee i will use an SNS.
Table 1. Deﬁnition of variables related to evacuee i and shelter j
VariableOioi
Di (j)Cj (t)Ii (j)FiUi

Set of candidates for evacuation destination for evacuee i
Current destination shelter of evacuee i
Distance between evacuee i and shelter jOvercapacity rate of shelter j at time t
Information of shelter j obtained by evacuee iState of receipt of false information and fact-checking information forevacuee i (Fi = 0 for all evacuees in initial state)
Probability that evacuee i will use an SNS(Initially, all users have a 10% probability of using an SNS.)2.3. SNS model. Our SNS model includes the following four functions similar to “Twitter”: tweet, retweet, timeline, and tweet search.
2.3.1. Tweet. The tweet is a function to post information Ii (j). An example of information
posted by the tweet function is presented in Figure 2. The posted information is stored
in the data frame illustrated in Figure 2: the information number in the ﬁrst column,
shelter name j in “Shelter”, information of shelter Ii (j) in “Information”, posted timein “Time”, name of evacuee i in “Evacuee”, and information number of the information
source in “Source”. Note that, in the case of the information posted by the tweet function,
0 is stored in “Source”. For example, the ﬁrst line of Figure 2 indicates that “at time 1,
evacuee 2041, posts information that shelter 3 is NOT crowded”.

Figure 2. Example of information posted by tweet function

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2.3.2. Retweet. The retweet is a function to repost existing information that was posted byanother person. An example of the information posted by the retweet function is depicted
in Figure 3. The information posted by the retweet function is stored in the same data
frame in Figure 2, but the information number of the information source is stored in“Source”. For example, the ﬁrst line of Figure 3 (information number 8) indicates that“at time 2, evacuee 2367, reposts information that shelter 1 is NOT crowded. The sourceof this information is information number 6”.

Figure 3. Examples of information posted by the retweet function. The
data frame is the same as in the tweet function (Figure 2) except for“Source”.
2.3.3. Timeline. The timeline is a function for viewing the information from other SNS
users, which the concerned SNS user, is following. In this study, we assumed that each
SNS user followed 500 other users. Minami and Kato [8] demonstrated that there was no
signiﬁcant diﬀerence in simulation results with and without users who had many followers.
Thus, in this study, the number of followers was constant for all evacuees. The timeline
displays the information of the SNS users that the concerned SNS user, is following, in theorder, of the most recent. When the evacuee, who is an SNS user, obtains the information
using the timeline function, the evacuee decides how much updated information to view.
Thereafter, the SNS user browses the information individually and copies the obtained
information to Ii (j). Note that we assumed that the timeline function was available att ≥ 30 because only the information of “Shelter is NOT crowded” may be posted at the
beginning of the evacuation.
2.3.4. Tweet search. The tweet search is a function to acquire information about thecongestion of the current destination shelter oi . If evacuee i, who is an SNS user, searches
for information regarding oi , evacuee i will obtain the latest information regarding oi andcopy its information to Ii (j). The tweet search function is available when at least one setof information regarding oi is posted.
2.4. Evacuation behavior. The evacuees continue to move towards shelters until they
complete the evacuation. If the set of the candidates for evacuation destinations for evacuee i is empty (Oi = ∅), evacuee i moves one step in their direction, as described in
Section 2.4.2. The other evacuees take the following actions at each step (t).
2.4.1. Obtaining information of shelter. If SNS is available, the evacuees can obtain information. In this study, we assumed that the evacuees obtain information by SNS witha 10% probability. The evacuees select the function to use from the timeline and tweet
search at random with a 50% probability when both are available, and repost the information with a 10% probability.
If evacuee i obtains information, evacuee i makes the following decision based on Ii (j).
• If shelter j is the current destination shelter of evacuee i (j = oi ):– If shelter j is crowded (Ii (j) = 0):evacuee i removes j from Oi and changes oi with a 50% probability.

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– If shelter j is somewhat crowded or shelter j is not crowded (Ii (j) = 1 or 2):
evacuee i changes nothing.
• If shelter j is not the current destination shelter of evacuee i (j ̸= oi ):– If shelter j is crowded (Ii (j) = 0):evacuee i removes j from Oi with a 10% probability.
– If shelter j is somewhat crowded (Ii (j) = 1):
evacuee i changes nothing.
– If shelter j is not crowded (Ii (j) = 2):
evacuee i changes oi to j with a 1/(Di (j) + 1) probability.
2.4.2. Behavior of evacuees. At the evacuation starting time (t = 0), we assume that
20% of the evacuees (i.e., 1,000 persons) know the location of their nearest shelter as
the destination shelter. The other evacuees take the following actions depending on theirsituation.
• If the evacuee ﬁnds another evacuee within a radius of 5 units who knows a destination shelter or has an evacuee to follow (deﬁned as a “leader”), he/she selects theevacuee ahead as his/her own leader to follow.
• If the evacuee does not ﬁnd anyone to follow, the evacuee changes the direction aheadslightly, by ±5 degrees.
The determination of arrival at the shelter is performed as follows.
• If evacuee i has already decided the destination shelter (oi ̸= 0):
if a shelter exists within a radius of 1 unit, we assume that evacuee i has arrived
at the shelter and performs the judgment of evacuation completion, as described in
Section 2.4.3. Otherwise, evacuee i moves one step in his/her direction.
• Although evacuee i is more than 1 unit away from any destination shelters anddoes not have a destination shelter (oi = 0), if a shelter exists within a radius of
5 units from evacuee i, the evacuee selects the shelter as the destination shelter oi .
Otherwise, evacuee i moves one step forward.
2.4.3. Judgment of evacuation completion. The judgment of evacuation completion is
performed according to whether or not evacuee i decides to enter the shelter on reaching
the shelter. Although the capacity of each shelter is 100 persons, evacuees are allowed toenter above the capacity, provided that the number remains, under the limit of allowablecongestion C i (t). For each evacuee i, the limit of allowable congestion is deﬁned as afunction of time C i (t), which indicates the congestion that is allowed for the shelter
capacity:t

C i (t) = C i0 × 2 k ,(3)where C i0 is the initial value of the limit of allowable congestion and this value diﬀers forevery evacuee. Moreover, k represents the tolerance of evacuees to the congestion that is
twice as much as C i0 . In this study, the value of C i0 for evacuee i was given by the normal
distribution with a mean of 1.5 and standard deviation of 0.1, and we set k = 1,000.
As evacuee i arrives at shelter j at time t, evacuee i compares the actual overcapacity
rate Cj (t) with his/her limit of allowable congestion C i (t) and makes a decision on whether
or not to enter the shelter as follows.
()• If evacuee i allows the congestion state of shelter j Cj (t) < C i (t) :evacuee i enters shelter j.
()• If evacuee i does not allow the congestion state of shelter j Cj (t) ≥ C i (t) :evacuee i does not enter shelter j. Furthermore, evacuee i excludes j from Oi andINT. J. INNOV. COMPUT. INF. CONTROL, VOL.18, NO.3, 2022

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deﬁnes the nearest shelter among the remaining shelters in Oi as the new destination
shelter oi . Subsequently, evacuee i continues his/her evacuation.
Moreover, if evacuee i is an SNS user and performs the judgment of the evacuation completion, evacuee i posts the congestion information of the shelter j with a 30% probability.
In this case, by comparing Cj (t) and C i (t), the following information is posted:0 : 0.8 × C i (t) ≤ Cj (t),(4)
Ii (j) = 1 : 0.2 × C i (t) ≤ Cj (t) < 0.8 × C i (t),
2 : C (t) < 0.2 × C (t).
ij
In Equation (4), it is considered that the posted information is not the objective congestion
rate, but the subjective perspective of the SNS user.
2.5. Transmission of false information and fact-checking. The simulation takes
account of sending out false information and fact-checking tweets on the SNS. During
the simulation, evacuees send out false information according to Section 2.5.1 and factchecking the tweets according to Section 2.5.2.
2.5.1. Transmission of false information and evacuee behavior. One evacuee i, who has
not completed evacuation, tweets the following false information only once:
Ii (j) = 3 : Shelter j is serving food,(5)

where shelter j in Equation (5) is randomly determined from shelters that are a distance of
100 or more units from the perspective of the false information sender i, regardless of theelements of Oi . Let jfake be the target shelter where the false information was sent. The
time of tweeting is set to the early stage of the disaster occurrence (the start of evacuation);that is, the time of tweeting false information is randomly determined between t = 30
and t = 100. It is assumed that the evacuee who sends out the false information obtains
the information by some other means and sends it out on the SNS without checking thefacts. Therefore, the sender is also deceived by the false information (Fi = 1). Even if
the sender is within a distance of 1 unit from the shelter, when the evacuee tweets thefalse information, the evacuation is continued by changing the destination of the shelter
to jfake , without making a judgment on the evacuation completion.
An evacuee i who sees the tweet of false information changes the destination shelter Oi
to jfake with a probability of 3/(Di (jfake ) + 3) (let jprev be the destination shelter before
the change). This probability is based on [8]. When the destination shelter is changed tojfake , the value of Fi is changed from 0 to 1. Based on the information stored in Ii (j), thefollowing decision is made.
• If shelter j is the current destination shelter of evacuee i (j = oi ):– If shelter j is crowded (Ii (j) = 0):evacuee i removes j from Oi with a 50% probability and reselects Oi . Furthermore, if Fi = 1, the probability Ui of using the SNS is changed from 1% to10%.
– If shelter j is serving food (Ii (j) = 3):change the probability Ui of using the SNS from 10% to 1% and then set Fi = 1.
– Otherwise, evacuee i changes nothing.
• If shelter j is not the current destination shelter of evacuee i (j ̸= oi ):– If shelter j is crowded (Ii (j) = 0):evacuee i removes j from Oi with a 10% probability.
– If shelter j is not crowded (Ii (j) = 2):
evacuee i changes oi to j with a 1/(Di (j) + 1) probability.

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H. TAKAHASHI, C. UENAE, Y. SUMITANI, Y. KAKIMOTO AND Y. OMAE

– If shelter j is serving food (Ii (j) = 3) and Fi = 0:evacuee i changes oi to jfake with a 3/(Di (j)+3) probability and then sets Fi = 1.
Furthermore, the probability Ui of using the SNS is changed from 10% to 1%.
– Otherwise, evacuee i changes nothing.
An evacuee, who sees the tweet of false information, retweets the false information tweet
with a 30% probability (normal tweets regarding shelter congestion have a 10% probabilityof being retweeted).
2.5.2. Fact-checking. When an evacuee i, who has seen a misinformation tweet (an evacuee with Fi = 1), arrives at shelter jfake , the following fact-checking tweet will be sentwith a 50% probability following the evacuation completion decision:
Ii (j) = 4 : Information that shelter j is serving food is incorrect.

(6)

When an evacuee i with Fi = 1 arrives at shelter jfake or sees a fact-checking tweet,
evacuee i changes Fi = 2. An evacuee, who changes the destination shelter to jfake owing
to a false information tweet (Fi = 1) and who also sees a fact-checking tweet, changes thedestination shelter from jfake to jprev with a probability 1 − {3/(Di (jfake ) + 3)}. If evacuee
i has changed the destination shelter from Oi = 0 (no destination shelter) to jfake , evacuee
i sets the destination shelter to Oi = 0. Moreover, the probability that the evacuee uses
the SNS, Ui , is changed from 1% to 10%. In summary, evacuee i makes the following
decisions based on the information stored in Ii (j).
• If shelter j is the current destination shelter of evacuee i (j = oi ):– If shelter j is crowded (Ii (j) = 0):evacuee i removes j from Oi with a 50% probability and reselects Oi . Moreover,
if Fi = 1, the probability Ui of using the SNS is changed from 1% to 10%.
– If shelter j is serving food (Ii (j) = 3):
the probability Ui of using the SNS is changed from 10% to 1% and Fi = 1 isset.
– If the information that shelter j is serving food is incorrect (Ii (j) = 4):
evacuee i changes oi to jprev with a 1 − {3/(Di (j) + 3)} probability and then sets
Fi = 2. Furthermore, the probability Ui of using the SNS is changed from 1% to10%.
– Otherwise, evacuee i changes nothing.
• If shelter j is not the current destination shelter of evacuee i (j ̸= oi ):– If shelter j is crowded (Ii (j) = 0):evacuee i removes j from Oi with a 10% probability.
– If shelter j is not crowded (Ii (j) = 2):
evacuee i changes oi to j with a 1/(Di (j) + 1) probability.
– If shelter j is serving food (Ii (j) = 3) and Fi = 0:evacuee i changes oi to jfake with a 3/(Di (j)+3) probability and then sets Fi = 1.
Furthermore, the probability Ui of using the SNS is changed from 10% to 1%.
– If the information that shelter j is serving food is incorrect (Ii (j) = 4) and
Fi = 0:evacuee i sets Fi = 2.
– Otherwise, evacuee i changes nothing.
Evacuee i who sees a fact-checking tweet will retweet the fact-checking tweet with a
30% probability (a normal tweet has a 10% probability of being retweeted). If evacuee
i with Fi = 2 ﬁnds a tweet of false information in a tweet search or timeline, evacuee iwill not read the tweet of false information, but will read the next tweet. Moreover, if
evacuee i with Fi = 2 ﬁnds a fact-checking tweet in a tweet search or timeline, evacuee

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i will retweet the fact-checking tweet only once. Thereafter, evacuee i will read the next
tweet without reading the fact-checking tweet.
3. Results and Discussion. Our simulation code was developed using R [10]. We
prepared 10 cities with 10 diﬀerent shelter distributions for the evacuation simulations.
There were 5,000 evacuees and 25 shelters in each city. The simulations were conducted for three cases: case A: the transmission of only congestion information, case B: thetransmission of congestion information and false information, and case C: the consideration of fact-checking in addition to the transmission of congestion information and false
information.
We calculated the following four evaluation indices based on previous research [8, 9].
The ﬁrst two, namely Lsum and Lmax , are related to the path length, which is equivalent toevacuation time, and the other two, σf and Rσ , are related to the variance of the numberof evacuees at shelters.
• Related to path length (= evacuation time):– Lsum : Summation of path lengths of all evacuees (i.e., average completion timefor evacuation)– Lmax : Maximum path length (i.e., (longest) completion time for evacuation)• Related to the balance of the number of evacuees among shelters:– σf : Final variance (i.e., variance of the number of evacuees at shelters at the endof the simulation)– Rσ : Ratio of the ﬁnal and maximum variance (i.e., σf /(maximum variance during
simulation))
As an example of the results, Figure 4 presents the mean values of the four evaluation
indices for 10 simulations in each case of City1. To investigate whether there was a signiﬁcant diﬀerence between the mean values of the evaluation indices for cases A, B, andC, a statistical test of the diﬀerence between the mean values was performed using theGames-Howell method.
It can be observed from Figures 4(c) and 4(d) that when false information was transmitted (case B), the mean values of the ﬁnal variance σf and ﬁnal/maximum variance ratio
Rσ increased signiﬁcantly compared to case A. This was because evacuees were directed tothe shelter with false information, and the number of evacuees that were accommodated
in the shelter was not balanced. Therefore, when the evacuees performed fact-checkingof the information (case C), these values, namely σf and Rσ , would settle at almost thesame level as in case A.
According to Figure 4(b), when false information was transmitted (case B), the meanvalue of Lmax , that is, the maximum time required for evacuation, decreased signiﬁcantly
compared to cases A and C.
As illustrated in Figure 5, a longer time was required to evacuate the last several dozen
people in cases A and C.
The evacuees who saw tweets of false information were more likely to change their evacuation destination and to retweet than those who saw tweets of congestion information(Section 2.5.1). This suggests that the time taken by the evacuees to decide on their evacuation destination and leader was shorter when false information was tweeted. Therefore,
Lmax in case B was smaller than that in cases A and C.
Moreover, the same tendency as that in cases A and C was evident. However, thenumber of evacuees, who were undecided about the destination shelter and leader of theevacuees, decreased slower in case C than in case A.
The evacuees who read the fact-checking tweets reverted their evacuation destination
shelter according to the distance from jfake , including the case in which the destination

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H. TAKAHASHI, C. UENAE, Y. SUMITANI, Y. KAKIMOTO AND Y. OMAE(a) Summation of path lengths Lsum(b) Maximum path length Lmax(c) Final variance σf(d) Ratio of ﬁnal and maximum variance RσFigure 4. City1 results as an example of simulation results. The meanvalues and standard deviations of the four evaluation indices for 10 simulations were obtained in each case. Moreover, a statistical test of the diﬀerence
between the mean values was performed using the Games-Howell method.
∗ means p < 0.05 and n.s. means not signiﬁcant.
evacuation shelter was reverted from jfake , to no destination evacuation shelter (oi = 0)
(Section 2.5.2). Let ifc be an evacuee who has changed the destination shelter from jfake
to no destination shelter (oi = 0) and who has no leader.
According to Section 2.4.2, the requirements for an evacuee to be a leader are that,the destination shelter is known (oi ̸= 0) or he/she has another evacuee as his/her leader.
Evacuee ifc will no longer satisfy the requirements for leader. Therefore, the evacuees who
were following ifc will not be able to ﬁnd the leader and are considered to have wanderedaround the area aimlessly with ifc .
We conﬁrmed that Lmax , σf , and Rσ exhibited a similar tendency for all cities. However,the summation of the path lengths of all evacuees (i.e., the average completion time for
evacuation), Lsum , exhibited a diﬀerent tendency in each city. This was owing to thestructure of the city. In the case of the city where the average distance between thecenter of the city and each shelter was small, the mean value of Lsum tended to decreasesigniﬁcantly in case B. However, in the case of the city where the average distance between
the center of the city and each shelter was large, the mean value of Lsum tended to increasesigniﬁcantly, as indicated in Figure 4(a).
4. Summary. To consider the exchange of information regarding shelters by SNSs, we
conducted a disaster evacuation simulation based on the assumption that evacuees used

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Figure 5. Number of cumulative evacuees, and number of evacuees whose
destination shelter and leader are unknown. This is an example result of asimulation case of City1.
SNSs to spread and collect congestion information of shelters, transmit false information,and fact-checking information.
The results demonstrated that, compared to the case of only spreading and collecting
congestion information, the mean value of the longest completion time for evacuation
decreased in the simulations that considered the transmission of false information. Moreover, the number of evacuees, that were accommodated, was not balanced among theevacuation shelters.
In the simulations that considered fact-checking transmission, the aforementioned problem of imbalance in the number of evacuees that were accommodated was eliminated byfact-checking, although the longest completion time for evacuation increased, comparedto the case in which only congestion information was transmitted. This indicates thatwhen evacuees use SNSs to send and obtain information voluntarily, they need to verify
the source of the information and send accurate information.
Regarding the average completion time for evacuation, as the results exhibited a different tendency in each city, it may be necessary to consider the relationship between the
average completion time for evacuation and the location of evacuation shelters in eachcity.
In this study, based on [8] and [9], we assumed some ﬁxed probabilities for the sake ofsimplicity, i.e., we used the ﬁxed probabilities discussed in Section 2 (e.g., the possibility
that the evacuees obtain information by SNS). Though the obtained results might notchange signiﬁcantly, it is important to conﬁrm the results, obtained by changing theseassumptions, quantitatively.
We did not consider the structures of speciﬁc cities or roads as we investigated the impact of the transmission of information using SNSs on the evacuation behavior. However,it can easily be observed that the structure of speciﬁc cities or roads is related to theevacuation behavior based on the results of the average completion time for evacuationin our simulations. This should be considered in future work.

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Moreover, we will perform more realistic simulations, e.g., a combination of SNSs andthe method of guiding evacuees, such as wireless-activated disaster warning systems andmass media.
Acknowledgments. This work was supported in part by JSPS Grant-in-Aid for Scientiﬁc Research (C) (Grant No. 20K12090; H. Takahashi).
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Author Biography
Hirotaka Takahashi received the D.Sci. degree from the Graduate School of Science and Technology, Niigata University, Japan, in 2005. He was a Postdoctoral
Researcher with the Max Planck Institute for Gravitational Physics (Albert Einstein
Institute), Germany, from September 2005 to August 2007. He has been a Faculty
Staﬀ Member at various universities. He has been a Professor with the ResearchCenter for Space Science, Advanced Research Laboratories, Tokyo City University,
Japan from September, 2020. His research interests include adaptive data analysis
methods, information theory, and gravitational wave physics and astronomy (generalrelativity). He is a member of the Institute of Electronics, Information and Communication Engineers (IEICE), the Information Processing Society of Japan (IPSJ),
the Operations Research Society of Japan (ORSJ), the Japan Society for Fuzzy Theory and Intelligent Informatics (J-SOFT) and the Physical Society of Japan (JPS),
among others.
Chie Uenae received the M.Eng. degree from the Department of Information andManagement Systems Engineering, Nagaoka University of Technology, Japan, in
2021. Her research interests include the development of the multi-agent systems andoperations research.

Yasushi Sumitani received the B.Eng. degree in Mechanical Engineering in 1991,
and the M.Eng. degree in Mechano Informatics in 1993, respectively, from Universityof Tokyo, Japan, and he obtained the M.A. degree in International and Development
Economics from Yale University, United States, in 2001. He worked in the Ministryof Economy, Trade and Industry, the Japanese Government, from 1993 to 2018, andhe was a full-time professor at the Nagaoka University of Technology from 2013 to2015. His current research interests include operations research, optimization, gametheory, value of information and decision-making.

Yohei Kakimoto received the B.Eng. and the M.Eng. degrees from Nagaoka University of Technology, Japan, in 2016 and 2018, respectively. He is currently an
Assistant of the College of Industrial Technology, Nihon University, Japan. His research interests include operations research and geographic information science. He
is a member of the Operations Research Society of Japan (ORSJ).

Yuto Omae received the D.Eng. degree from the Department of Information Science and Control Engineering, Nagaoka University of Technology, Japan in 2016.
He was a Postdoctoral Researcher at Japan Institute of Sports Sciences and an Assistant Professor at National Institute of Technology, Tokyo College. He is currently
an Assistant Professor of Department of Industrial Engineering and Management,College of Industrial Technology, Nihon University, Japan from April 2019. His research interests are intelligent informatics, machine learning, mathematical modeland so on. He is a member of the Institute of Electrical and Electronics Engineers(IEEE), the Institute of Electronics, Information and Communication Engineers (IEICE), the Japanese Society for Artiﬁcial Intelligence (JSAI), the Japan Society for
Fuzzy Theory and Intelligent Informatics (J-SOFT), among others.